Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs

A combined deep-learning model detecting CPPD at the TFCC and MCP-2/3 joints in hand radiographs provides the highest diagnostic performance. The algorithm could be used to screen larger OA or RA databases or electronic medical records for CPPD cases. Future work includes dataset expansion and validation with external datasets.

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Publication Details

Journal
Frontiers in Medicine
Published
2024-10-23
DOI
https://doi.org/10.3389/fmed.2024.1431333
Citations
3
Primary Topic
Rheumatoid Arthritis Research and Therapies
Type
article
Field-Weighted Citation Impact
1.41

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article

Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs

Guillaume Fahrni, Thomas Hügle, Fabio Becce, Tobias Manigold et al.
3 citations
Frontiers in Medicine
Rheumatoid Arthritis Research and Therapies
1.41
article

Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs

Guillaume Fahrni, Thomas Hügle, Fabio Becce, Tobias Manigold, E. Rosoux, Deborah Markham
article en
3 citations

Abstract

A combined deep-learning model detecting CPPD at the TFCC and MCP-2/3 joints in hand radiographs provides the highest diagnostic performance. The algorithm could be used to screen larger OA or RA databases or electronic medical records for CPPD cases. Future work includes dataset expansion and validation with external datasets.

Frontiers in MedicineVol. 11
University of Bern (CH), University Hospital of Bern (CH), University of Lausanne (CH)
Université de Lausanne
Good health and well-being
Openalex Percentile: Top 17%
Rheumatoid Arthritis Research and Therapies
1.41
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Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs — Guillaume Fahrni, Thomas Hügle, et al. · Frontiers in Medicine (2024) | TGRS Research Map | TGRS